Test html widget ouput

require(oetteR)
require(tidyverse)

## Loading required package: tidyverse

## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr

## Conflicts with tidy packages ----------------------------------------------

## filter():  dplyr, stats
## is_null(): purrr, testthat
## lag():     dplyr, stats
## matches(): dplyr, testthat

col_codes = mtcars %>%
  f_clean_data() %>%
  f_boxcox() %>%
  f_plot_color_code_variables()

## [1] "Number of excluded observations: 0"

## Warning: Unknown variables: `null`

ls = f_clean_data(mtcars) %>%
 f_boxcox() %>%
 f_pca() %>%
 f_pca_plot_components()

## [1] "Number of excluded observations: 0"

## Warning: Unknown variables: `null`

## Warning in .f(.x[[i]], ...): argument is not a matrix, returning input
## object

## Warning in .f(.x[[i]], ...): argument is not a matrix, returning input
## object

## Warning in .f(.x[[i]], ...): argument is not a matrix, returning input
## object

## Joining, by = c("row_names", "PC")

ls

f_clean_data(mtcars) %>%
f_boxcox() %>%
f_pca() %>%
f_pca_plot_variance_explained()

## [1] "Number of excluded observations: 0"

## Warning: Unknown variables: `null`

## Warning in .f(.x[[i]], ...): argument is not a matrix, returning input
## object

## Warning in .f(.x[[i]], ...): argument is not a matrix, returning input
## object

## Warning in .f(.x[[i]], ...): argument is not a matrix, returning input
## object

#pipelearner
  

pl = mtcars %>%
  f_clean_data() %>%
  .$data %>%
  pipelearner::pipelearner(.$data) %>%
  pipelearner::learn_models( twidlr::rpart, disp~. ) %>%
  pipelearner::learn_models( twidlr::randomForest, disp~. ) %>%
  pipelearner::learn_models( twidlr::svm, disp~. ) %>%
  pipelearner::learn() %>%
  mutate( imp = map2(fit, train, f_model_importance)
         , plot = pmap( list(imp, model, models.id, cv_pairs.id, train_p)
                       , f_model_importance_plot
                       , variable_color_code = col_codes
                       , 'pipelearner'
                       )
         )

## [1] "Number of excluded observations: 0"

## Warning: Unknown variables: `null`

## Warning: Unknown or uninitialised column: 'data'.

## Warning: `cross_d()` is deprecated; please use `cross_df()` instead.

## Warning: `cross_d()` is deprecated; please use `cross_df()` instead.

## Warning: `cross_d()` is deprecated; please use `cross_df()` instead.

## Warning: `cross_d()` is deprecated; please use `cross_df()` instead.

## Warning: `cross_d()` is deprecated; please use `cross_df()` instead.

## Warning: `cross_d()` is deprecated; please use `cross_df()` instead.

## Warning in if (class(M) == "model") {: the condition has length > 1 and
## only the first element will be used

## Warning in if (class(M) == "model") Y = predict(M, data[(1:L), ]) else Y =
## PRED(M, : the condition has length > 1 and only the first element will be
## used

## Warning in if (class(M) == "model") Y = predict(M, data[(1:L), ]) else Y =
## PRED(M, : the condition has length > 1 and only the first element will be
## used

## Warning in if (class(M) == "model") Y = predict(M, data[(1:L), ]) else Y =
## PRED(M, : the condition has length > 1 and only the first element will be
## used

## Warning in if (class(M) == "model") Y = predict(M, data[(1:L), ]) else Y =
## PRED(M, : the condition has length > 1 and only the first element will be
## used

## Warning in if (class(M) == "model") Y = predict(M, data[(1:L), ]) else Y =
## PRED(M, : the condition has length > 1 and only the first element will be
## used

## Warning in if (class(M) == "model") Y = predict(M, data[(1:L), ]) else Y =
## PRED(M, : the condition has length > 1 and only the first element will be
## used

## Warning in if (class(M) == "model") Y = predict(M, data[(1:L), ]) else Y =
## PRED(M, : the condition has length > 1 and only the first element will be
## used

## Warning in if (class(M) == "model") Y = predict(M, data[(1:L), ]) else Y =
## PRED(M, : the condition has length > 1 and only the first element will be
## used

## Warning in if (class(M) == "model") Y = predict(M, data[(1:L), ]) else Y =
## PRED(M, : the condition has length > 1 and only the first element will be
## used

## Warning in if (class(M) == "model") Y = predict(M, data[(1:L), ]) else Y =
## PRED(M, : the condition has length > 1 and only the first element will be
## used

## Warning in if (class(M) == "model") Y = predict(M, data[(1:L), ]) else Y =
## PRED(M, : the condition has length > 1 and only the first element will be
## used

## Joining, by = "variables"

## Warning: Column `variables` joining factor and character vector, coercing
## into character vector

## Joining, by = "variables"

## Warning: Column `variables` joining factor and character vector, coercing
## into character vector

## Joining, by = "variables"

## Warning: Column `variables` joining factor and character vector, coercing
## into character vector

htmltools::tagList(pl$plot)